@inproceedings{adc6d7dac2cf4e78a27143da338741ca,
title = "Real-time Fusion and Object Detection Based on Visible Light and Infrared Images",
abstract = "Autonomous driving faces challenges like object tracking and recognition in adverse weather and low-light environments. Traditional visible light-based object detection algorithms fail in adverse weather conditions such as low light, heavy rain, and fog. Therefore, an object detection method that is applicable to different weather conditions is needed. This paper aims to enhance object detection and tracking performance in autonomous driving by integrating visible light and infrared cameras. Several object detection and tracking algorithms are evaluated. Experimental results demonstrate the effectiveness of multimodal fusion in improving detection accuracy in real-time under challenging environmental conditions.",
keywords = "Data Fusion, Infrared Image, Object Detection and Tracking, Visible Light Image",
author = "Jingliang Zhang and Yong Yue and Xiaohui Zhu",
note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 2025 9th International Conference on Control Engineering and Artificial Intelligence, CCEAI 2025 ; Conference date: 16-01-2025 Through 19-01-2025",
year = "2025",
month = may,
day = "13",
doi = "10.1145/3722150.3722160",
language = "English",
series = "Proceedings of 2025 9th International Conference on Control Engineering and Artificial Intelligence, CCEAI 2025",
publisher = "Association for Computing Machinery, Inc",
pages = "59--64",
editor = "Zhang Dan and Yue Yong and Marek Ogiela",
booktitle = "Proceedings of 2025 9th International Conference on Control Engineering and Artificial Intelligence, CCEAI 2025",
}